{"ID":5443906,"CreatedAt":"2026-07-01T02:07:11.383974684Z","UpdatedAt":"2026-07-03T17:47:04.346850254Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.32026","arxiv_id":"2606.32026","title":"AdaJEPA: An Adaptive Latent World Model","abstract":"Latent world models enable planning from high-dimensional observations by predicting future states in a compact latent space. However, these models are typically kept frozen at test time: when their predictions become inaccurate, planning can fail, especially under test-time distribution shift. To address this, we propose AdaJEPA, an adaptive latent world model that performs test-time adaptation within the closed loop of model predictive control (MPC). After training, AdaJEPA plans and executes the first action chunk, uses the observed next-state transition as a self-supervised adaptation signal, and replans with the updated model. This closed-loop update continuously recalibrates the world model without additional expert demonstrations. Across a range of goal-reaching tasks, AdaJEPA substantially improves planning success with as few as one gradient step per MPC replanning step.","short_abstract":"Latent world models enable planning from high-dimensional observations by predicting future states in a compact latent space. However, these models are typically kept frozen at test time: when their predictions become inaccurate, planning can fail, especially under test-time distribution shift. To address this, we prop...","url_abs":"https://arxiv.org/abs/2606.32026","url_pdf":"https://arxiv.org/pdf/2606.32026v1","authors":"[\"Ying Wang\",\"Oumayma Bounou\",\"Yann LeCun\",\"Mengye Ren\"]","published":"2026-06-30T17:53:48Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[]","has_code":false}